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Resource Discovery Using NeuroSearch

Presentation for the Agora Center InBCT-seminar 13.11.2003

Mikko Vapa, researcherInBCT 3.2 Cheese Factory / P2P Communication

Agora Center

http://tisu.it.jyu.fi/cheesefactory

2003

UNIVERSITY OF JYVÄSKYLÄ

Resource Discovery Problem

• In peer-to-peer (P2P) resource discovery problem any node in the network can possess resources and also query these resources from other nodes

Node1: Where is ?

Node 1

Node 2

Node 3

Node 4

2003

UNIVERSITY OF JYVÄSKYLÄ

Resource Discovery Problem

• The problem is multi-dimensional because:

– Location of resources can be arbitrary, it is not known beforehand and often changes constantly

– Topology can be arbitrary and it might also change between each query

– The querying node’s location in the network is arbitrary

– Sufficient number of replies might depend on the queried resource

• It is enough to locate one instance of a file or a processor, but the performance usually speeds linearly up when multiple instances are located

– Some of the nodes might not behave as expected

• For example some of the queries might be dropped by adversarial nodes requiring multiple query paths to be used

• When the number of dimensions increase problem becomes complex

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A Simple Solution for the Problem

• Gnutella P2P network for example uses Breadth-First Search (BFS) flooding algorithm which sends query to all neighbors

• Problems: all resources in the network can be found, but network gets congested and there are lots of useless packets

Node 1: Where is ?

Node 1

Node 2

Node 3

Node 4

Query

QueryQuery

Query

Query

Query

Node 4: I have it!

Node 2: I have it!Node 4: Node 4 has it too!Reply

Reply

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Our solution: NeuroSearch

• NeuroSearch resource discovery algorithm uses neural networks and evolution to adapt its’ behavior to given environment– neural network for deciding whether to pass the query further

down the link or not– evolution for breeding and finding out the best neural

network in a large class of local search algorithms

Query

Forward the query

Forward the query

Neighbor Node

Neighbor Node

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NeuroSearch’s Inputs• The internal structure of NeuroSearch algorithm

• Multiple layers enable the algorithm to express non-linear behavior

• With enough neurons the algorithm can universally approximate any decision function

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NeuroSearch’s Training Program

• The neural network weights define how neural network behaves so they must be adjusted to right values

• This is done using iterative optimization process based on evolution and Gaussian mutation

Define thenetwork

conditions

Define the fitness requirements

for the algorithm

Create candidate algorithmsrandomly

Select the bestones for next

generation

Breed a newpopulation

Finally select thebest algorithm forthese conditions

Iteratethousands

ofgenerations

Compare the bestone against other

local search algorithms

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Benefits

• Universal: Whatever the peer-to-peer network conditions are a feasible solution algorithm can be found

• Zero-configurable: There is no parameters that a designer would need to tune by hand

• Supports various requirements: Designer can define what kind of an algorithm he/she wants to have

• Rapid development: Even designing a simple algorithm for P2P network might take many months by human designer while with NeuroSearch this time is only couple of hours

• Efficient: Multiple search strategies may in some cases be the only viable choice for example in mobile peer-to-peer networks where the environment is changing all the time

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Well How Good Is The Algorithm?

• We defined a peer-to-peer network scenario where:– 100 nodes form a power-law distributed P2P

network having few hubs and lots of low-connectivity nodes

– Resources are distributed based on the number of connections the node has meaning that high-connectivity nodes are more likely to answer to the queries

– Topology is static meaning that nodes are not moving• Then we made a wish list for the algorithm and hoped that:

– The algorithm should locate half of the available resources for every query

– The algorithm should use as minimal number of packets as possible

– The algorithm should always stop

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Well How Good Is The Algorithm?

• After a week we were ready to compare NeuroSearch’s invention against Breadth-First Search in 50-query test scenario

• The measurements indicate that the optimization process can find an algorithm that:– finds half of the resources in the network with high probability– locates more resources than BFS with maximum number of 2

hops (BFS-2), but sill fewer than BFS-3– consumes a bit more packets than BFS-2, but significantly

less than BFS-3– adapts to the peer-to-peer environment taking advantage of

the environments resource distributions and topological features

Conclusion is that the approach is feasible, but not yet optimal

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Performance of NeuroSearch – Hit Rate

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Performance of NeuroSearch - Replies

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Performance of NeuroSearch - Packets

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Typical query pattern of NeuroSearch

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Future Work

• Now the first version of NeuroSearch is ready and analyzed• The short-range future work of NeuroSearch includes (master’s thesis

topics):– Comparison to other existing resource discovery algorithms– Reduction of resource discovery problem to route discovery problem

and analysis of behavior in dynamic conditions– Analysis of the effects of increasing NeuroSearch’s brain power and

peer-to-peer network size• New input types to feed NeuroSearch with more information• More neurons to allow NeuroSearch to make wiser decisions• Studying the scalability factors affecting NeuroSearch when the

network size grows– Hybridization of evolutionary optimization method with local

optimization method

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Future Work

• The long-range future work of NeuroSearch includes: – Parallelization of evolutionary optimization method for speeding up the

convergence time (free master’s thesis topic) – Multicriteria objective functions for NeuroSearch for maximizing the

lifetime of battery powered mobile P2P networks (free master’s thesis topic)

– Development of new ad hoc protocol based on NeuroSearch (free dissertation topic)

– Combination of topology management algorithm with NeuroSearch for finding optimal P2P network (dissertation topic)

– Extension of NeuroSearch to ontology-based queries and reduction of query traffic using varying ontologies (dissertation topic)

– Study of NeuroSearch’s performance under attack, random failure and deceptive scenarios (free dissertation topic)

– Online adaptation of NeuroSearch in real P2P environment (free dissertation topic)

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References

• Vapa M., Kotilainen N., Kainulainen H., Vuori J., ”Resource Discovery in P2P Networks Using Evolutionary Neural Networks”, submitted to IEE Electronics Letters, June 2003

• Vapa M., Kotilainen N., Auvinen A., Töyrylä J., Hyytiälä H., Vuori J., ”NeuroSearch: evolutionary neural network resource discovery algorithm for peer-to-peer networks”, being submitted to Elsevier Science Ad Hoc Networks Journal, November 2003

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Thank You!

Any questions?